The other main consideration for training with a PSS is the patch schedule that sets the value of \(\rho \)  at each training iteration. Here, we take inspiration from work on learning rate schedules and model several patch schedulers according to learning rate schedules. The schedulers using in our evaluation are depicted in Figure REF . The baseline schedule (blue \(\bullet \) line) uses 100% of patches (\(\rho =1.0\) ) for all training iterations. In our implementation, if \(\rho =1.0\) , then we skip the patch sampling block entirely. A Fixed PSS (orange \(\blacktriangle \)  line) uses the same percentage of patches (i.e., 60%) each iteration. This is useful for comparison against other schedules that vary \(\rho \)  across iterations. A Linear PSS (green \(\blacktriangledown \)  line) linearly increases the number of patches kept across iterations. Note that we use a step function to reduce the chance of irregular dimensions (e.g., 37 patches per image) that can lead to worse running time. Finally, we use a Cyclic PSS (red \(\blacksquare \) line) inspired by cyclical learning rates [1]}. Unlike the other schedules, the Cyclic schedule has a period of a single epoch, varying \(\rho \)  from 0.2 to 1.0. For all schedules settings, we aim to use roughly the same amount of training time in order to fairly compare each approach.
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